{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#竞赛的评价指标为logloss\n",
    "from sklearn.metrics import log_loss  \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5b66f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.Pregnancies);\n",
    "plt.xlabel('Pregnancies');\n",
    "plt.ylabel('Number of occurrences');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8d6f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"Glucose\"].values,color='purple')\n",
    "plt.title(\"Glucose\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9d3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"BloodPressure\"].values,color='purple')\n",
    "plt.title(\"BloodPressure\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9ef7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"SkinThickness\"].values,color='purple')\n",
    "plt.title(\"SkinThickness\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "227\n"
     ]
    }
   ],
   "source": [
    "print(data[data[\"SkinThickness\"] == 0].shape[0]) #零值行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba8d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"Insulin\"].values,color='purple')\n",
    "plt.title(\"Insulin\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "374\n"
     ]
    }
   ],
   "source": [
    "print(data[data[\"Insulin\"] == 0].shape[0]) #零值行数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbaf3048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"BMI\"].values,color='purple')\n",
    "plt.title(\"BMI\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb48c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"DiabetesPedigreeFunction\"].values,color='purple')\n",
    "plt.title(\"DiabetesPedigreeFunction\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4e5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data[\"Age\"].values,color='purple')\n",
    "plt.title(\"Age\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb4ec6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data.Outcome);\n",
    "plt.xlabel('Outcome');\n",
    "plt.ylabel('Number of occurrences');\n",
    "#类别不平衡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据预处理和特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>33.600000</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>29.00000</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>26.600000</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.494673</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>35.00000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>116.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>78.0</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>115.0</td>\n",
       "      <td>72.405184</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>35.300000</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>197.0</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>45.00000</td>\n",
       "      <td>543.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>125.0</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>32.457464</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>0.191</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>168.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.537</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>139.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>27.100000</td>\n",
       "      <td>1.441</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>30.100000</td>\n",
       "      <td>0.398</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>166.0</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>25.800000</td>\n",
       "      <td>0.587</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>72.405184</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.484</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.494673</td>\n",
       "      <td>118.0</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>47.00000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>45.800000</td>\n",
       "      <td>0.551</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>107.0</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>29.15342</td>\n",
       "      <td>155.548223</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>0.254</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>103.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>38.00000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>43.300000</td>\n",
       "      <td>0.183</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.0</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>34.600000</td>\n",
       "      <td>0.529</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pregnancies  Glucose  BloodPressure  SkinThickness     Insulin        BMI  \\\n",
       "0      6.000000    148.0      72.000000       35.00000  155.548223  33.600000   \n",
       "1      1.000000     85.0      66.000000       29.00000  155.548223  26.600000   \n",
       "2      8.000000    183.0      64.000000       29.15342  155.548223  23.300000   \n",
       "3      1.000000     89.0      66.000000       23.00000   94.000000  28.100000   \n",
       "4      4.494673    137.0      40.000000       35.00000  168.000000  43.100000   \n",
       "5      5.000000    116.0      74.000000       29.15342  155.548223  25.600000   \n",
       "6      3.000000     78.0      50.000000       32.00000   88.000000  31.000000   \n",
       "7     10.000000    115.0      72.405184       29.15342  155.548223  35.300000   \n",
       "8      2.000000    197.0      70.000000       45.00000  543.000000  30.500000   \n",
       "9      8.000000    125.0      96.000000       29.15342  155.548223  32.457464   \n",
       "10     4.000000    110.0      92.000000       29.15342  155.548223  37.600000   \n",
       "11    10.000000    168.0      74.000000       29.15342  155.548223  38.000000   \n",
       "12    10.000000    139.0      80.000000       29.15342  155.548223  27.100000   \n",
       "13     1.000000    189.0      60.000000       23.00000  846.000000  30.100000   \n",
       "14     5.000000    166.0      72.000000       19.00000  175.000000  25.800000   \n",
       "15     7.000000    100.0      72.405184       29.15342  155.548223  30.000000   \n",
       "16     4.494673    118.0      84.000000       47.00000  230.000000  45.800000   \n",
       "17     7.000000    107.0      74.000000       29.15342  155.548223  29.600000   \n",
       "18     1.000000    103.0      30.000000       38.00000   83.000000  43.300000   \n",
       "19     1.000000    115.0      70.000000       30.00000   96.000000  34.600000   \n",
       "\n",
       "    DiabetesPedigreeFunction   Age  \n",
       "0                      0.627  50.0  \n",
       "1                      0.351  31.0  \n",
       "2                      0.672  32.0  \n",
       "3                      0.167  21.0  \n",
       "4                      2.288  33.0  \n",
       "5                      0.201  30.0  \n",
       "6                      0.248  26.0  \n",
       "7                      0.134  29.0  \n",
       "8                      0.158  53.0  \n",
       "9                      0.232  54.0  \n",
       "10                     0.191  30.0  \n",
       "11                     0.537  34.0  \n",
       "12                     1.441  57.0  \n",
       "13                     0.398  59.0  \n",
       "14                     0.587  51.0  \n",
       "15                     0.484  32.0  \n",
       "16                     0.551  31.0  \n",
       "17                     0.254  31.0  \n",
       "18                     0.183  33.0  \n",
       "19                     0.529  32.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.drop(['Outcome'], axis=1);\n",
    "y = data['Outcome'];\n",
    "\n",
    "from sklearn.preprocessing import Imputer\n",
    "imp = Imputer(missing_values=0, strategy='mean', axis=0, verbose=0, copy=True)\n",
    "X = pd.DataFrame(imp.fit_transform(X))\n",
    "X.columns = ['Pregnancies','Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
    "X.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行标准化处理\n",
    "X = ss_X.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# 初始化特征的归一化器\n",
    "mms_X = MinMaxScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征进行归一化处理\n",
    "X = mms_X.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一、Logistic回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n",
       "          fit_intercept=True, intercept_scaling=1, max_iter=100,\n",
       "          multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "          solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#需要调优的参数\n",
    "# 请尝试将L1正则和L2正则分开，并配合合适的优化求解算法（slover）\n",
    "#tuned_parameters = {'penalty':['l1','l2'],\n",
    "#                   'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "#                   }\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression(class_weight='balanced')\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00080004, 0.00079999, 0.00059996, 0.00079999, 0.00139995,\n",
       "        0.00099998, 0.00379996, 0.00120001, 0.00640001, 0.00119996,\n",
       "        0.00519996, 0.00120006, 0.00620003, 0.00180001]),\n",
       " 'mean_score_time': array([0.00079999, 0.00059996, 0.00100002, 0.00079994, 0.00080009,\n",
       "        0.00039997, 0.00079994, 0.0006    , 0.00079994, 0.0006    ,\n",
       "        0.0006    , 0.00100002, 0.00159998, 0.0006    ]),\n",
       " 'mean_test_score': array([-0.69314718, -0.69423108, -0.69314718, -0.68941938, -0.62796089,\n",
       "        -0.62134607, -0.51863998, -0.52987256, -0.51860725, -0.51742716,\n",
       "        -0.51916507, -0.51893378, -0.51922875, -0.5192041 ]),\n",
       " 'mean_train_score': array([-0.69314718, -0.69419173, -0.69314718, -0.68905503, -0.62678873,\n",
       "        -0.61878066, -0.50523807, -0.52049008, -0.49961656, -0.50029072,\n",
       "        -0.49951121, -0.49952187, -0.49951141, -0.49951116]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([12, 14, 12, 11, 10,  9,  3,  8,  2,  1,  5,  4,  7,  6]),\n",
       " 'split0_test_score': array([-0.69314718, -0.69426077, -0.69314718, -0.68976661, -0.62368969,\n",
       "        -0.62487232, -0.52985919, -0.54304823, -0.53729146, -0.53565911,\n",
       "        -0.5386478 , -0.53833394, -0.538794  , -0.53875644]),\n",
       " 'split0_train_score': array([-0.69314718, -0.69416198, -0.69314718, -0.68839601, -0.61619795,\n",
       "        -0.61328717, -0.49186498, -0.50873906, -0.48651395, -0.48722322,\n",
       "        -0.48645742, -0.48645932, -0.48646274, -0.48646173]),\n",
       " 'split1_test_score': array([-0.69314718, -0.69396613, -0.69314718, -0.68762154, -0.62532297,\n",
       "        -0.61084868, -0.47963477, -0.49862155, -0.46996104, -0.47175678,\n",
       "        -0.46954225, -0.46966267, -0.46948418, -0.46949443]),\n",
       " 'split1_train_score': array([-0.69314718, -0.69425146, -0.69314718, -0.69009359, -0.64099659,\n",
       "        -0.62606832, -0.52201554, -0.5353547 , -0.51696927, -0.51752337,\n",
       "        -0.51690488, -0.51691269, -0.5169077 , -0.51690711]),\n",
       " 'split2_test_score': array([-0.69314718, -0.69457349, -0.69314718, -0.69154532, -0.63461847,\n",
       "        -0.62949673, -0.52608909, -0.53790479, -0.5253538 , -0.52340881,\n",
       "        -0.52611461, -0.52575106, -0.52620796, -0.52617004]),\n",
       " 'split2_train_score': array([-0.69314718, -0.69418758, -0.69314718, -0.68906209, -0.62639279,\n",
       "        -0.61970188, -0.51104226, -0.52465731, -0.50384977, -0.50455395,\n",
       "        -0.50375791, -0.50376473, -0.50376099, -0.50375987]),\n",
       " 'split3_test_score': array([-0.69314718, -0.69448648, -0.69314718, -0.69104147, -0.64052055,\n",
       "        -0.63132286, -0.5820568 , -0.57073308, -0.59137963, -0.58449894,\n",
       "        -0.5926451 , -0.59177069, -0.59279138, -0.59270568]),\n",
       " 'split3_train_score': array([-0.69314718, -0.69419193, -0.69314718, -0.68869   , -0.62009143,\n",
       "        -0.61470723, -0.4898148 , -0.50822722, -0.48386557, -0.48477781,\n",
       "        -0.48367178, -0.48370174, -0.48366525, -0.48366656]),\n",
       " 'split4_test_score': array([-0.69314718, -0.69386741, -0.69314718, -0.68711355, -0.61568983,\n",
       "        -0.61015119, -0.47563479, -0.49902945, -0.46908745, -0.47183859,\n",
       "        -0.46890596, -0.4691805 , -0.46889599, -0.46892373]),\n",
       " 'split4_train_score': array([-0.69314718, -0.69416572, -0.69314718, -0.68903345, -0.63026488,\n",
       "        -0.6201387 , -0.51145276, -0.52547213, -0.50688422, -0.50737524,\n",
       "        -0.50676406, -0.50677085, -0.50676035, -0.50676053]),\n",
       " 'std_fit_time': array([0.00040002, 0.00074835, 0.00048986, 0.00039999, 0.00048982,\n",
       "        0.00063241, 0.00039997, 0.00040004, 0.00195962, 0.00039995,\n",
       "        0.00040004, 0.00040002, 0.00097987, 0.00039992]),\n",
       " 'std_score_time': array([3.99994861e-04, 4.89862441e-04, 1.16800773e-07, 3.99971008e-04,\n",
       "        4.00042573e-04, 4.89862441e-04, 3.99971008e-04, 4.89901382e-04,\n",
       "        3.99971008e-04, 4.89901382e-04, 4.89901382e-04, 6.32485093e-04,\n",
       "        1.35646611e-03, 4.89901382e-04]),\n",
       " 'std_test_score': array([0.        , 0.00027743, 0.        , 0.00177665, 0.00867297,\n",
       "        0.00908701, 0.03883158, 0.02764756, 0.04575142, 0.04241407,\n",
       "        0.04641499, 0.04601762, 0.04649079, 0.04645135]),\n",
       " 'std_train_score': array([0.00000000e+00, 3.20821330e-05, 0.00000000e+00, 5.73878036e-04,\n",
       "        8.61535996e-03, 4.52933175e-03, 1.24137914e-02, 1.05034059e-02,\n",
       "        1.25827477e-02, 1.24638652e-02, 1.26052930e-02, 1.26007710e-02,\n",
       "        1.26063888e-02, 1.26060554e-02])}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.51742716102493\n",
      "{'C': 10, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "l2正则，正则参数=10时最佳"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc5f550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#pd.DataFrame(grid.cv_results_).to_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#cvresult = pd.DataFrame.from_csv('LogisticGridSearchCV_Otto.csv')\n",
    "#test_means = cv_results['mean_test_score']\n",
    "#test_stds = cv_results['std_test_score'] \n",
    "#train_means = cvresult['mean_train_score']\n",
    "#train_stds = cvresult['std_train_score'] \n",
    "\n",
    "\n",
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二、线性SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC2 =  LinearSVC( C = C,class_weight = 'balanced')\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.4090909090909091\n",
      "accuracy: 0.7532467532467533\n",
      "accuracy: 0.7532467532467533\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7987012987012987\n",
      "accuracy: 0.7272727272727273\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbec2eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "plt.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('SVM.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C=100时最好。可以看到线性SVM的效果比Logistic回归要好。这可能由于SVM计算的分类平面仅取决于支持向量对应的几个数据点，受类别不平衡的负面影响较小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三、RBF核SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma, class_weight = 'balanced')\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC3.score(X_val, y_val)\n",
    "    \n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7012987012987013\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.7077922077922078\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.7922077922077922\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.7142857142857143\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7272727272727273\n",
      "accuracy: 0.7077922077922078\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7857142857142857\n",
      "accuracy: 0.7597402597402597\n",
      "accuracy: 0.7142857142857143\n",
      "accuracy: 0.7077922077922078\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7597402597402597\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7532467532467533\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.7077922077922078\n",
      "accuracy: 0.6948051948051948\n"
     ]
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-3, 3, 7)\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbad1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1 =np.array(accuracy_s).reshape(len(C_s),len(gamma_s))\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    plt.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('RBF_SVM.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = 1,gamma = 1时效果最好"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
